Investigating Flux Rope Eruptivity via Time-dependent Data-driven Modelling

Data-driven coronal models are attracting increasing attention for their ability to accurately capture the pre-eruption magnetic field configuration of active regions. However, the degree to which the current modelling techniques are able to provide information on the loss of stability and initial dynamics of the eruptions remains unclear. An interesting avenue for probing this is by employing time-dependent modelling such that the dynamic data-driving is switched-off at a given time. In this study, we investigate what we can learn from this relaxation procedure about the eruption itself and the instability that ultimately triggers it for at least two different active regions. To this effect, we use the time-dependent data-driven magnetofrictional model (TMFM) and perform multiple runs with varying relaxation times (i.e., time instances when the driving is switched off). Furthermore, we use two different physical models to simulate the coronal evolution after this point in time: the standard magnetofrictional method and a zero-beta MHD (magnetohydrodynamics) approach. In case of an eruption being triggered, the detailed evolution is characterised by tracking the associated magnetic flux rope which is extracted from the simulation data with a semi-automatic extraction algorithm. This flux rope detection and tracking procedure makes use of the twist number T_w, as well as the morphological gradient. For a further improvement of the extraction procedure, various mathematical morphology algorithms are performed to accurately extract the flux rope field lines. The properties of the extracted flux ropes are compared against their observational low-coronal manifestation in SDO/AIA data.